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https://github.com/PaddlePaddle/FastDeploy.git
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6343b0db47
* Add notes for tensors * Optimize some apis * move some warnings * Support build with Paddle2ONNX * Add protobuf support * Fix compile on mac * add clearn package script * Add paddle2onnx code * remove submodule * Add onnx ocde * remove softlink * add onnx code * fix error * Add cmake file * fix patchelf * update paddle2onnx * Delete .gitmodules --------- Co-authored-by: PaddleCI <paddle_ci@example.com> Co-authored-by: pangyoki <pangyoki@126.com> Co-authored-by: jiangjiajun <jiangjiajun@baidu.lcom>
80 lines
2.6 KiB
C++
80 lines
2.6 KiB
C++
/*
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* SPDX-License-Identifier: Apache-2.0
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*/
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// ATTENTION: The code in this file is highly EXPERIMENTAL.
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// Adventurous users should note that the APIs will probably change.
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#pragma once
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#include "onnxoptimizer/pass.h"
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namespace ONNX_NAMESPACE {
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namespace optimization {
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struct FuseConsecutiveTransposes final : public PredicateBasedPass {
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explicit FuseConsecutiveTransposes()
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: PredicateBasedPass(PassType::Fuse, PassEfficiency::Complete,
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PassOptimizationType::Compute) {}
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std::string getPassName() const override {
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return "fuse_consecutive_transposes";
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}
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// returns a vector `ret` such that transposing by `ret` is equivalent
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// to transposing by `t1` and then by `t2`
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std::vector<int64_t> compose_transposes(const std::vector<int64_t>& t1,
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const std::vector<int64_t>& t2) {
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ONNX_ASSERT(t1.size() == t2.size());
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std::vector<int64_t> ret;
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ret.reserve(t1.size());
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for (size_t i = 0; i < t1.size(); i++) {
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ONNX_ASSERT(t2[i] < static_cast<int64_t>(t1.size()));
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ONNX_ASSERT(t1[static_cast<size_t>(t2[i])] <
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static_cast<int64_t>(t1.size()));
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ret.push_back(t1[static_cast<size_t>(t2[i])]);
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}
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return ret;
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}
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bool patternMatchPredicate(Node* node) override {
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return node->kind() == kTranspose &&
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node->input()->node()->kind() == kTranspose;
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}
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bool runTransform(Node* n, Graph&,
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NodeDestroyType& destroy_current) override {
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auto origInput = n->input();
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if (!n->hasAttribute(kperm) && !origInput->node()->hasAttribute(kperm)) {
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// One special case (two consecutive transposes with no perm,
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// since we do not have the shape information here, we have
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// to eliminate two transpose together.
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if (n->output()->has_sizes()) {
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origInput->node()->input()->setSizes(n->output()->sizes());
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}
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const bool replacing_success =
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tryReplacingAllUsesWith(n, origInput->node()->input()->node());
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if (!replacing_success) {
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return false;
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}
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destroy_current = NodeDestroyType::DestroyTwo;
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return true;
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}
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if (!n->hasAttribute(kperm) || !origInput->node()->hasAttribute(kperm)) {
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destroy_current = NodeDestroyType::DestroyZero;
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return false;
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}
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n->is_(kperm,
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compose_transposes(origInput->node()->is(kperm), n->is(kperm)));
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n->replaceInput(0, origInput->node()->input());
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if (origInput->uses().size() == 0) {
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origInput->node()->destroy();
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}
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destroy_current = NodeDestroyType::DestroyZero;
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return false;
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}
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};
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} // namespace optimization
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} // namespace ONNX_NAMESPACE
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